Estimation of Species Richness Using Bayesian Networks

We propose a new methodology based on continuous Bayesian networks for assessing species richness. Specifically, we applied a restricted structure Bayesian network, known as tree augmented naive Bayes, regarding a set of environmental continuous predictors. Firstly, we analyzed the relationships between the response variable (called the terrestrial vertebrate species richness) and a set of environmental predictors. Secondly, the learnt model was used to estimate the species richness in Andalusia (Spain) and the results were depicted on a map. The model managed to deal with the species richness environment relationship, which is complex from the ecological point of view. The results highlight that landscape heterogeneity, topographical and social variables had a direct relationship with species richness while climatic variables showed more complicated relationships with the response.